Articles | Volume 17, issue 9
Nat. Hazards Earth Syst. Sci., 17, 1683–1696, 2017
https://doi.org/10.5194/nhess-17-1683-2017

Special issue: Damage of natural hazards: assessment and mitigation

Nat. Hazards Earth Syst. Sci., 17, 1683–1696, 2017
https://doi.org/10.5194/nhess-17-1683-2017
Research article
29 Sep 2017
Research article | 29 Sep 2017

Multi-variable flood damage modelling with limited data using supervised learning approaches

Dennis Wagenaar et al.

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Latest update: 28 Sep 2022
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Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
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